Revealing routes of cellular differentiation by single-cell RNA-seq

Differentiation of multipotent stem cells is controlled by the intricate regulatory interactions of thousands of genes. It remains one of the major challenges to understand how nature has designed such robust and reproducible regulatory mechanisms. Knowing the detailed structure of the underlying lineage trees is the basis for investigating the molecular control of this process. The recent availability of large-scale sensitive single-cell RNAseq protocols has enabled the generation of snapshot data covering the entire spectrum of cell states in a systemof interest. Consequently, a large number of computational methods for the reconstruction of cellular differentiation trajectories have been developed. Here, I will provide a detailed overview of the concepts and ideas behind some of these algorithms and discuss the particular aspects addressed by each method

Transcriptome-based Gene Networks for Systems-level Analysis of Plant Gene Functions

Present day genomic technologies are evolving at an unprecedented rate, allowing interrogation of cellular activities with increasing breadth and depth. However, we know very little about how the genome functions and what the identified genes do. The lack of functional annotations of genes greatly limits the post-analytical interpretation of new high throughput genomic datasets. For plant biologists, the problem is much severe. Less than 50% of all the identified genes in the model plant Arabidopsis thaliana, and only about 20% of all genes in the crop model Oryza sativa have some aspects of their functions assigned. Therefore, there is an urgent need to develop innovative methods to predict and expand on the currently available functional annotations of plant genes. With open-access catching the ‘pulse’ of modern day molecular research, an integration of the copious amount of transcriptome datasets allows rapid prediction of gene functions in specific biological contexts, which provide added evidence over traditional homology-based functional inference. The main goal of this dissertation was to develop data analysis strategies and tools broadly applicable in systems biology research. Two user friendly interactive web applications are presented: The Rice Regulatory Network (RRN) captures an abiotic-stress conditioned gene regulatory network designed to facilitate the identification of transcription factor targets during induction of various environmental stresses. The Arabidopsis Seed Active Network (SANe) is a transcriptional regulatory network that encapsulates various aspects of seed formation, including embryogenesis, endosperm development and seed-coat formation. Further, an edge-set enrichment analysis algorithm is proposed that uses network density as a parameter to estimate the gain or loss in correlation of pathways between two conditionally independent coexpression networks

Global gene expression profiling of healthy human brain and its application in studying neurological disorders

The human brain is the most complex structure known to mankind and one of the greatest challenges in modern biology is to understand how it is built and organized. The power of the brain arises from its variety of cells and structures, and ultimately where and when different genes are switched on and off throughout the brain tissue. In other words, brain function depends on the precise regulation of gene expression in its sub-anatomical structures. But, our understanding of the complexity and dynamics of the transcriptome of the human brain is still incomplete. To fill in the need, we designed a gene expression model that accurately defines the consistent blueprint of the brain transcriptome; thereby, identifying the core brain specific transcriptional processes conserved across individuals. Functionally characterizing this model would provide profound insights into the transcriptional landscape, biological pathways and the expression distribution of neurotransmitter systems. Here, in this dissertation we developed an expression model by capturing the similarly expressed gene patterns across congruently annotated brain structures in six individual brains by using data from the Allen Brain Atlas (ABA). We found that 84% of genes are expressed in at least one of the 190 brain structures. By employing hierarchical clustering we were able to show that distinct structures of a bigger brain region can cluster together while still retaining their expression identity. Further, weighted correlation network analysis identified 19 robust modules of coexpressing genes in the brain that demonstrated a wide range of functional associations. Since signatures of local phenomena can be masked by larger signatures, we performed local analysis on each distinct brain structure. Pathway and gene ontology enrichment analysis on these structures showed, striking enrichment for brain region specific processes. Besides, we also mapped the structural distribution of the gene expression profiles of genes associated with major neurotransmission systems in the human. We also postulated the utility of healthy brain tissue gene expression to predict potential genes involved in a neurological disorder, in the absence of data from diseased tissues. To this end, we developed a supervised classification model, which achieved an accuracy of 84% and an AUC (Area Under the Curve) of 0.81 from ROC plots, for predicting autism-implicated genes using the healthy expression model as the baseline. This study represents the first use of healthy brain gene expression to predict the scope of genes in autism implication and this generic methodology can be applied to predict genes involved in other neurological disorders

High-Throughput Polygenic Biomarker Discovery Using Condition-Specific Gene Coexpression Networks

Biomarkers can be described as molecular signatures that are associated with a trait or disease. RNA expression data facilitates discovery of biomarkers underlying complex phenotypes because it can capture dynamic biochemical processes that are regulated in tissue-specific and time-specific manners. Gene Coexpression Network (GCN) analysis is a method that utilizes RNA expression data to identify binary gene relationships across experimental conditions. Using a novel GCN construction algorithm, Knowledge Independent Network Construction (KINC), I provide evidence for novel polygenic biomarkers in both plant and animal use cases. Kidney cancer is comprised of several distinct subtypes that demonstrate unique histological and molecular signatures. Using KINC, I have identified gene correlations that are specific to clear cell renal cell carcinoma (ccRCC), the most common form of kidney cancer. ccRCC is associated with two common mutation profiles that respond differently to targeted therapy. By identifying GCN edges that are specific to patients with each of these two mutation profiles, I discovered unique genes with similar biological function, suggesting a role for T cell exhaustion in the development of ccRCC. Medicago truncatula is a legume that is capable of atmospheric nitrogen fixation through a symbiotic relationship between plant and rhizobium that results in root nodulation. This process is governed by complex gene expression patterns that are dynamically regulated across tissues over the course of rhizobial infection. Using de novo RNA sequencing data generated from the root maturation zone at five distinct time points, I identified hundreds of genes that were differentially expressed between control and inoculated plants at specific time points. To discover genes that were co-regulated during this experiment, I constructed a GCN using the KINC software. By combining GCN clustering analysis with differentially expressed genes, I present evidence for novel root nodulation biomarkers. These biomarkers suggest that temporal regulation of pathogen response related genes is an important process in nodulation. Large-scale GCN analysis requires computational resources and stable data-processing pipelines. Supercomputers such as Clemson University’s Palmetto Cluster provide data storage and processing resources that enable terabyte-scale experiments. However, with the wealth of public sequencing data available for mining, petabyte-scale experiments are required to provide novel insights across the tree of life. I discuss computational challenges that I have discovered with large scale RNA expression data mining, and present two workflows, OSG-GEM and OSG-KINC, that enable researchers to access geographically distributed computing resources to handle petabyte-scale experiments

TRAPLINE: a standardized and automated pipeline for RNA sequencing data analysis, evaluation and annotation

Background: Technical advances in Next Generation Sequencing (NGS) provide a means to acquire deeper insights into cellular functions. The lack of standardized and automated methodologies poses a challenge for the analysis and interpretation of RNA sequencing data. We critically compare and evaluate state-of-the-art bioinformatics approaches and present a workflow that integrates the best performing data analysis, data evaluation and annotation methods in a Transparent, Reproducible and Automated PipeLINE (TRAPLINE) for RNA sequencing data processing (suitable for Illumina, SOLiD and Solexa). Results: Comparative transcriptomics analyses with TRAPLINE result in a set of differentially expressed genes, their corresponding protein-protein interactions, splice variants, promoter activity, predicted miRNA-target interactions and files for single nucleotide polymorphism (SNP) calling. The obtained results are combined into a single file for downstream analysis such as network construction. We demonstrate the value of the proposed pipeline by characterizing the transcriptome of our recently described stem cell derived antibiotic selected cardiac bodies ('aCaBs'). Conclusion: TRAPLINE supports NGS-based research by providing a workflow that requires no bioinformatics skills, decreases the processing time of the analysis and works in the cloud. The pipeline is implemented in the biomedical research platform Galaxy and is freely accessible via www.sbi.uni-rostock.de/RNAseqTRAPLINE or the specific Galaxy manual page (https://usegalaxy.org/u/mwolfien/p/trapline-manual)

Utilizing gene co-expression networks for comparative transcriptomic analyses

The development of high-throughput technologies such as microarray and next-generation RNA sequencing (RNA-seq) has generated numerous transcriptomic data that can be used for comparative transcriptomics studies. Transcriptomes obtained from different species can reveal differentially expressed genes that underlie species-specific traits. It also has the potential to identify genes that have conserved gene expression patterns. However, differential expression alone does not provide information about how the genes relate to each other in terms of gene expression or if groups of genes are correlated in similar ways across species, tissues, etc. This makes gene expression networks, such as co-expression networks, valuable in terms of finding similarities or differences between genes based on their relationships with other genes. The desired outcome of this research was to develop methods for comparative transcriptomics, specifically for comparing gene co-expression networks (GCNs), either within or between any set of organisms. These networks represent genes as nodes in the network, and pairs of genes may be connected by an edge representing the strength of the relationship between the pairs. We begin with a review of currently utilized techniques available that can be used or adapted to compare gene co-expression networks. We also work to systematically determine the appropriate number of samples needed to construct reproducible gene co-expression networks for comparison purposes. In order to systematically compare these replicate networks, software to visualize the relationship between replicate networks was created to determine when the consistency of the networks begins to plateau and if this is affected by factors such as tissue type and sample size. Finally, we developed a tool called Juxtapose that utilizes gene embedding to functionally interpret the commonalities and differences between a given set of co-expression networks constructed using transcriptome datasets from various organisms. A set of transcriptome datasets were utilized from publicly available sources as well as from collaborators. GTEx and Gene Expression Omnibus (GEO) RNA-seq datasets were used for the evaluation of the techniques proposed in this research. Skeletal cell datasets of closely related species and more evolutionarily distant organisms were also analyzed to investigate the evolutionary relationships of several skeletal cell types. We found evidence that data characteristics such as tissue origin, as well as the method used to construct gene co-expression networks, can substantially impact the number of samples required to generate reproducible networks. In particular, if a threshold is used to construct a gene co-expression network for downstream analyses, the number of samples used to construct the networks is an important consideration as many samples may be required to generate networks that have a reproducible edge order when sorted by edge weight. We also demonstrated the capabilities of our proposed method for comparing GCNs, Juxtapose, showing that it is capable of consistently matching up genes in identical networks, and it also reflects the similarity between different networks using cosine distance as a measure of gene similarity. Finally, we applied our proposed method to skeletal cell networks and find evidence of conserved gene relationships within skeletal GCNs from the same species and identify modules of genes with similar embeddings across species that are enriched for biological processes involved in cartilage and osteoblast development. Furthermore, smaller sub-networks of genes reflect the phylogenetic relationships of the species analyzed using our gene embedding strategy to compare the GCNs. This research has produced methodologies and tools that can be used for evolutionary studies and generalizable to scenarios other than cross-species comparisons, including co-expression network comparisons across tissues or conditions within the same species

Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model

Background: Obesity is a complex metabolic condition in strong association with various diseases, like type 2 diabetes, resulting in major public health and economic implications. Obesity is the result of environmental and genetic factors and their interactions, including genome-wide genetic interactions. Identification of co-expressed and regulatory genes in RNA extracted from relevant tissues representing lean and obese individuals provides an entry point for the identification of genes and pathways of importance to the development of obesity. The pig, an omnivorous animal, is an excellent model for human obesity, offering the possibility to study in-depth organ-level transcriptomic regulations of obesity, unfeasible in humans. Our aim was to reveal adipose tissue co-expression networks, pathways and transcriptional regulations of obesity using RNA Sequencing based systems biology approaches in a porcine model. Methods: We selected 36 animals for RNA Sequencing from a previously created F2 pig population representing three extreme groups based on their predicted genetic risks for obesity. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to detect clusters of highly co-expressed genes (modules). Additionally, regulator genes were detected using Lemon-Tree algorithms. Results: WGCNA revealed five modules which were strongly correlated with at least one obesity-related phenotype (correlations ranging from -0.54 to 0.72, P <0.001). Functional annotation identified pathways enlightening the association between obesity and other diseases, like osteoporosis (osteoclast differentiation, P = 1.4E(-7)), and immune-related complications (e. g. Natural killer cell mediated cytotoxity, P = 3.8E(-5); B cell receptor signaling pathway, P = 7.2E(-5)). Lemon-Tree identified three potential regulator genes, using confident scores, for the WGCNA module which was associated with osteoclast differentiation: CCR1, MSR1 and SI1 (probability scores respectively 95.30, 62.28, and 34.58). Moreover, detection of differentially connected genes identified various genes previously identified to be associated with obesity in humans and rodents, e.g. CSF1R and MARC2. Conclusions: To our knowledge, this is the first study to apply systems biology approaches using porcine adipose tissue RNA-Sequencing data in a genetically characterized porcine model for obesity. We revealed complex networks, pathways, candidate and regulatory genes related to obesity, confirming the complexity of obesity and its association with immune-related disorders and osteoporosis

Integrative Genomics Implicates Disruption Of Prenatal Neurogenesis In Congenital Hydrocephalus

Congenital Hydrocephalus (CH) affects 1/1000 live births and costs the US healthcare system over $2 billion annually. Mainstay therapies, hinging on surgical cerebrospinal fluid diversion, exhibit high failure rates and substantial morbidity. Limited understanding of pathogenesis warrants identification of crucial genetic drivers underlying CH and their impact on brain development. This pioneering study integrates gene discovery from the largest whole-exome sequenced CH cohort with transcriptional networks (modules) and cell-type markers from the latest transcriptomic atlases of the mid-gestational human brain to uncover the genomic and molecular architecture of CH. Exome analysis of 381 radiographically-confirmed, neurosurgically-treated sporadic CH probands (including 232 case-parent trios) identified genes with rare de novo or transmitted mutations conferring disease risk. Transcriptome analyses identified mid-gestational brain modules and cell-types enriched for cohort-determined CH risk genes, known genes previously implicated in isolated and syndromic forms of CH, and risk genes of Autism Spectrum Disorder (ASD) and Developmental Disorder (DD). Genetic drivers of CH converge in a neurodevelopmental network and in early neurogenic cell-types, implicating genetic disruption of early brain development as a primary patho-mechanism for a significant subset of CH patients. Genetic and transcriptional overlap with ASD and DD may explain persistence of these conditions in CH patients despite surgical intervention, while greater potency of CH-enriched neural precursors may account for increased frequency of structural brain abnormalities in CH than in ASD or DD alone